Curr Top Med Chem. 2023 Jan 30. doi: 10.2174/1568026623666230130150029. Online ahead of print.

ABSTRACT

Drug repurposing is a strategy used to develop new treatments based on approved or investigational drugs outside the scope of their original clinical indication. Since this approach benefits from the original toxicity data of the repurposed drugs, the drug-repurposing strategy is time-saving, and inexpensive. It has a higher success rate compared to traditional drug discovery. Several repurposing candidates have been identified in silico screening and in vitro methodologies. One of the best examples is non-steroidal anti-inflammatory drugs [NSAIDs]. Tumor-promoting inflammation is one of the hallmarks of cancer, revealing a connection between inflammatory processes and tumor progression and development. This explains why using NSAIDs in the context of neoplasia has become a topic of interest. Indeed, identifying NSAIDs with antitumor activity has become a promising strategy for finding novel cancer treatment opportunities. Indeed, several commercial anti-inflammatory drugs, including aspirin, ibuprofen, diclofenac, celecoxib, tepoxalin and cyclovalone, naproxen, and indomethacin have presented antitumor activity, and some of them are already in clinical trials for cancer treatment. However, the benefits and complications of using NSAIDs for cancer treatment must be carefully evaluated, particularly for cancer patients with no further therapeutic options available. This review article provides insight into the drug repurposing strategy and describes some of the well-known NSAIDs that have been investigated as repurposed drugs with potential anticancer activity.

PMID:36717997 | DOI:10.2174/1568026623666230130150029

J Periodontol. 2023 Jan 30. doi: 10.1002/JPER.22-0535. Online ahead of print.

ABSTRACT

BACKGROUND: Silibinin has shown various pharmacological effects could be attributed to its antioxidant, anti-inflammatory, and immunoregulatory properties. However, the therapeutic potential of silibinin for periodontitis has not been investigated.

METHODS: The therapeutic effects of silibinin in ligation-induced experimental periodontitis were investigated using biochemical, histological, and immunohistochemical methods. The effects of silibinin on the osteoclastogenesis of RAW264.7 cells were investigated using TRAP staining, qPCR, pit formation, and immunoblotting. Moreover, its effects on inflammatory cytokine production, RANKL expression, and oxidative stress in lipopolysaccharide (LPS)-stimulated human gingival fibroblasts (HGFs) were evaluated using qPCR, and flow cytometry. A coculture system was established to elucidate the effects of silibinin on the crosstalk between LPS-stimulated HGFs and undifferentiated monocytes.

RESULTS: Silibinin significantly reduced the alveolar bone loss, decreased the gingival inflammation and RANKL expression, and decreased the RANKL/OPG ratio in gingival tissues in experimental periodontitis. The in vitro results showed that silibinin inhibited RANKL-induced osteoclast differentiation and function of RAW264.7 cells, suppressed RANKL-induced NFATc1 induction and translocation through the NF-κB and MAPK signaling pathways. Silibinin decreased the inflammatory cytokine level and oxidative stress production in LPS-stimulated HGFs; significantly supressed membrane-bound RANKL expression on LPS-stimulated HGFs; and significantly disrupted TRAP+ cell differentiation in the coculture system.

CONCLUSIONS: Silibinin effectively inhibits inflammation-induced bone loss in experimental periodontitis based on the regulation of stimulated HGFs by inhibiting the expression of inflammatory and osteoclastogenic mediators. Collectively, targeting the inflamed HGF resolution that mediates osteogenesis may employ silibinin as a potential drug repurposing candidate for modulating alveolar bone destruction in periodontitis. This article is protected by copyright. All rights reserved.

PMID:36716169 | DOI:10.1002/JPER.22-0535

Emerg Microbes Infect. 2023 Jan 30:2174777. doi: 10.1080/22221751.2023.2174777. Online ahead of print.

ABSTRACT

Zika virus (ZIKV) infections are typically asymptomatic but cause severe neurological complications (e.g. Guillain-Barré syndrome in adults, and microcephaly in newborns). There are currently no specific therapy or vaccine options available to prevent ZIKV infections. Temporal gene expression profiles of ZIKV-infected human brain microvascular endothelial cells (HBMECs) were used in this study to identify genes essential for viral replication. These genes were then used to identify novel anti-ZIKV agents and validated in publicly available data and functional wet-lab experiments. Here, we found that ZIKV effectively evaded activation of immune response-related genes and completely reprogrammed cellular transcriptional architectures. Knockdown of genes, which gradually upregulated during viral infection but showed distinct expression patterns between ZIKV- and mock infection, discovered novel proviral and antiviral factors. One-third of the 74 drugs found through signature-based drug repositioning and cross-reference with the Drug Gene Interaction Database (DGIdb) were known anti-ZIKV agents. In cellular assays, two promising antiviral candidates (Luminespib/NVP-AUY922, L-161982) were found to reduce viral replication without causing cell toxicity. Overall, our time-series transcriptome-based methods offer a novel and feasible strategy for antiviral drug discovery. Our strategies, which combine conventional and data-driven analysis, can be extended for other pathogens causing pandemics in the future.

PMID:36715162 | DOI:10.1080/22221751.2023.2174777

bioRxiv. 2023 Jan 6:2023.01.05.522939. doi: 10.1101/2023.01.05.522939. Preprint.

ABSTRACT

Important tasks in biomedical discovery such as predicting gene functions, gene-disease associations, and drug repurposing opportunities are often framed as network edge prediction. The number of edges connecting to a node, termed degree, can vary greatly across nodes in real biomedical networks, and the distribution of degrees varies between networks. If degree strongly influences edge prediction, then imbalance or bias in the distribution of degrees could lead to nonspecific or misleading predictions. We introduce a network permutation framework to quantify the effects of node degree on edge prediction. Our framework decomposes performance into the proportions attributable to degree and the network's specific connections. We discover that performance attributable to factors other than degree is often only a small portion of overall performance. Degree's predictive performance diminishes when the networks used for training and testing-despite measuring the same biological relationships-were generated using distinct techniques and hence have large differences in degree distribution. We introduce the permutation-derived edge prior as the probability that an edge exists based only on degree. The edge prior shows excellent discrimination and calibration for 20 biomedical networks (16 bipartite, 3 undirected, 1 directed), with AUROCs frequently exceeding 0.85. Researchers seeking to predict new or missing edges in biological networks should use the edge prior as a baseline to identify the fraction of performance that is nonspecific because of degree. We released our methods as an open-source Python package ( https://github.com/hetio/xswap/ ).

PMID:36711569 | PMC:PMC9881952 | DOI:10.1101/2023.01.05.522939

bioRxiv. 2023 Jan 7:2023.01.05.522941. doi: 10.1101/2023.01.05.522941. Preprint.

ABSTRACT

Hetnets, short for "heterogeneous networks", contain multiple node and relationship types and offer a way to encode biomedical knowledge. One such example, Hetionet connects 11 types of nodes - including genes, diseases, drugs, pathways, and anatomical structures - with over 2 million edges of 24 types. Previous work has demonstrated that supervised machine learning methods applied to such networks can identify drug repurposing opportunities. However, a training set of known relationships does not exist for many types of node pairs, even when it would be useful to examine how nodes of those types are meaningfully connected. For example, users may be curious not only how metformin is related to breast cancer, but also how the GJA1 gene might be involved in insomnia. We developed a new procedure, termed hetnet connectivity search, that proposes important paths between any two nodes without requiring a supervised gold standard. The algorithm behind connectivity search identifies types of paths that occur more frequently than would be expected by chance (based on node degree alone). We find that predictions are broadly similar to those from previously described supervised approaches for certain node type pairs. Scoring of individual paths is based on the most specific paths of a given type. Several optimizations were required to precompute significant instances of node connectivity at the scale of large knowledge graphs. We implemented the method on Hetionet and provide an online interface at https://het.io/search . We provide an open source implementation of these methods in our new Python package named hetmatpy .

PMID:36711546 | PMC:PMC9882000 | DOI:10.1101/2023.01.05.522941

Virus Res. 2023 Jan 26:199053. doi: 10.1016/j.virusres.2023.199053. Online ahead of print.

ABSTRACT

Coronavirus disease 2019 (COVID-19) is an acute viral disease with millions of cases worldwide. Although the number of daily new cases and deaths has been dropping, there is still a need for therapeutic alternatives to deal with severe cases. A promising strategy to prospect new therapeutic candidates is to investigate the regulatory mechanisms involved in COVID-19 progression using integrated transcriptomics approaches. In this work, we aimed to identify COVID-19 Master Regulators (MRs) using a series of publicly available gene expression datasets of lung tissue from patients which developed the severe form of the disease. We were able to identify a set of six potential COVID-19 MRs related to its severe form, namely TAL1, TEAD4, EPAS1, ATOH8, ERG, and ARNTL2. In addition, using the Connectivity Map drug repositioning approach, we identified 52 different drugs which could be used to revert the disease signature, thus being candidates for the design of novel clinical treatments. Furthermore, we compared the identified signature and drugs with the ones obtained from the analysis of nasopharyngeal swab samples from infected patients and preclinical cell models. This comparison showed significant similarities between them, although also revealing some limitations on the overlap between clinical and preclinical data in COVID-19, highlighting the need for careful selection of the best model for each disease stage.

PMID:36709793 | DOI:10.1016/j.virusres.2023.199053

J Biomol Struct Dyn. 2023 Jan 29:1-14. doi: 10.1080/07391102.2023.2171135. Online ahead of print.

ABSTRACT

Most of the existing DENV entry inhibitors were discovered through structure-based, high-throughput screening techniques and optimization approaches by aiming β-OG pocket. However, the class of precise chemical scaffolds with superior antiviral activity targeting the early stages of virus infection that is considered to be beneficial in therapeutics and is still in process. In this study, ligand-based pharmacophore modeling using existing DENV entry inhibitors provided two best models, AADRR-2 and AAADR-2 (A- accepter, D- donor, R-ring) to screen public and DrugBank datasets. Further, approximately 36000 molecules were filtered using Zinc13 by employing the ideal validated models. Additionally, using β-OG binding pocket as target site, molecular docking experiments including induced-fit studies were conducted that provided further structurally divergent ligands. Moreover, the refined list of preferential hits were filtered out based on the best fitness score, binding energy and interaction paradigm, among them fused pyrimidine, hydrazone and biphenyl core comprising scaffolds were identified possessing profound interaction profile with key amino acid residues, ALA-50, GLN-200, PHE-193 and PHE-279 in 100 ns MD simulations. Additionally, the search for similar chemical fingerprints from DrugBank library was also carried out and Eltrombopag (Promacta/Revolade® prescribed in thrombocytopenia) was identified as a preferential β-OG pocket binder. The identified pyrazole-based hydrazone class of drug, Eltrombopag is in phase II clinical trials employed to treat dengue-mediated thrombocytopenia.Communicated by Ramaswamy H. Sarma.

PMID:36709443 | DOI:10.1080/07391102.2023.2171135

Clin Epigenetics. 2023 Jan 28;15(1):15. doi: 10.1186/s13148-023-01427-7.

ABSTRACT

BACKGROUND: N-methyl-2-pyrrolidone (NMP) is an epigenetically active chemical fragment and organic solvent with numerous applications including use as a drug-delivery vehicle. Previously considered biologically inert, NMP demonstrates immunomodulatory and anti-myeloma properties that are partly explained by acetyllysine mimetic properties and non-specific bromodomain inhibition. We therefore evaluated orally administered NMP in a phase 1 dose-escalation trial to establish its maximum tolerated dose (MTD) in patients with relapsed/refractory multiple myeloma (RR-MM). Secondary endpoints were safety, pharmacokinetics (PK), overall response rate and immunological biomarkers of activity.

RESULTS: Thirteen patients received NMP at starting doses between 50 and 400 mg daily. Intra-patient dose escalation occurred in five patients, with one attaining the ceiling protocolised dose of 1 g daily. Median number of monthly cycles commenced was three (range 1-20). Grade 3-4 adverse events (AEs) were reported in seven (54%; 95% CI 25-81%) patients. Most common AEs (> 30% of patients) of any grade were nausea and musculoskeletal pain. The only dose limiting toxicity (DLT) was diarrhoea in a patient receiving 200 mg NMP (overall DLT rate 8%; 95% CI 0-36%). Hence, the MTD was not defined. Median progression-free and overall survival were 57 (range 29-539) days and 33 (95% CI 9.7- > 44) months, respectively. The best response of stable disease (SD) was achieved in nine patients (69%; 95% CI 39-91%). PK analysis demonstrated proportional dose-concentrations up to 400 mg daily, with a more linear relationship above 500 mg. Maximum plasma concentrations (Cmax) of 16.7 mg/L at the 800 mg dose were below those predicted to inhibit BET-bromodomains. Peripheral blood immune-profiling demonstrated maintenance of natural killer (NK) cells, and a gene expression signature suggestive of enhanced T, B and NK cell functions; a subject with prolonged exposure manifested sustained recovery of B and NK cells at 12 months.

CONCLUSIONS: NMP demonstrated potential disease stabilising and immunomodulatory activity at sub-BET inhibitory plasma concentrations and was well tolerated in RR-MM; an MTD was not determined up to a maximum dose of 1 g daily. Further dose-finding studies are required to optimise NMP dosing strategies for therapeutic intervention.

PMID:36709310 | DOI:10.1186/s13148-023-01427-7

J Antibiot (Tokyo). 2023 Jan 27. doi: 10.1038/s41429-023-00594-2. Online ahead of print.

ABSTRACT

Epidemic diseases of crops caused by fungi deeply affected the course of human history and processed a major restriction on social and economic development. However, with the enormous misuse of existing antimicrobial drugs, an increasing number of fungi have developed serious resistance to them, making the diseases caused by pathogenic fungi even more challenging to control. Drug repurposing is an attractive alternative, it requires less time and investment in the drug development process than traditional R&D strategies. In this work, we screened 600 existing commercially available drugs, some of which had previously unknown activity against pathogenic fungi. From the primary screen at a fixed concentration of 100 μg/mL, 120, 162, 167, 85, 102, and 82 drugs were found to be effective against Rhizoctonia solani, Sclerotinia sclerotiorum, Botrytis cinerea, Phytophthora capsici, Fusarium graminearum and Fusarium oxysporum, respectively. They were divided into nine groups lead compounds, including quinoline alkaloids, benzimidazoles/carbamate esters, azoles, isothiazoles, pyrimidines, pyridines, piperidines/piperazines, ionic liquids and miscellaneous group, and simple structure-activity relationship analysis was carried out. Comparison with fungicides to identify the most promising drugs or lead structures for the development of new antifungal agents in agriculture.

PMID:36707717 | DOI:10.1038/s41429-023-00594-2

Sci Rep. 2023 Jan 27;13(1):1494. doi: 10.1038/s41598-023-28716-8.

ABSTRACT

After over two years of living with Covid-19 and hundreds of million cases worldwide there is still an unmet need to find proper treatments for the novel coronavirus, due also to the rapid mutation of its genome. In this context, a drug repositioning study has been performed, using in silico tools targeting Delta Spike protein/ACE2 interface. To this aim, it has been virtually screened a library composed by 4388 approved drugs through a deep learning-based QSAR model to identify protein-protein interactions modulators for molecular docking against Spike receptor binding domain (RBD). Binding energies of predicted complexes were calculated by Molecular Mechanics/Generalized Born Surface Area from docking and molecular dynamics simulations. Four out of the top twenty ranking compounds showed stable binding modes on Delta Spike RBD and were evaluated also for their effectiveness against Omicron. Among them an antihistaminic drug, fexofenadine, revealed very low binding energy, stable complex, and interesting interactions with Delta Spike RBD. Several antihistaminic drugs were found to exhibit direct antiviral activity against SARS-CoV-2 in vitro, and their mechanisms of action is still debated. This study not only highlights the potential of our computational methodology for a rapid screening of variant-specific drugs, but also represents a further tool for investigating properties and mechanisms of selected drugs.

PMID:36707679 | DOI:10.1038/s41598-023-28716-8

Nat Genet. 2023 Jan 26. doi: 10.1038/s41588-022-01282-x. Online ahead of print.

ABSTRACT

Most transcriptome-wide association studies (TWASs) so far focus on European ancestry and lack diversity. To overcome this limitation, we aggregated genome-wide association study (GWAS) summary statistics, whole-genome sequences and expression quantitative trait locus (eQTL) data from diverse ancestries. We developed a new approach, TESLA (multi-ancestry integrative study using an optimal linear combination of association statistics), to integrate an eQTL dataset with a multi-ancestry GWAS. By exploiting shared phenotypic effects between ancestries and accommodating potential effect heterogeneities, TESLA improves power over other TWAS methods. When applied to tobacco use phenotypes, TESLA identified 273 new genes, up to 55% more compared with alternative TWAS methods. These hits and subsequent fine mapping using TESLA point to target genes with biological relevance. In silico drug-repurposing analyses highlight several drugs with known efficacy, including dextromethorphan and galantamine, and new drugs such as muscle relaxants that may be repurposed for treating nicotine addiction.

PMID:36702996 | DOI:10.1038/s41588-022-01282-x

Eur J Med Chem. 2023 Jan 18;249:115129. doi: 10.1016/j.ejmech.2023.115129. Online ahead of print.

ABSTRACT

The 3C-like protease (3CLpro) is essential for the replication and transcription of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), making it a promising target for the treatment of corona virus disease 2019 (COVID-19). In this study, a series of 2,3,5-substituted [1,2,4]-thiadiazole analogs were discovered to be able to inhibit 3CLpro as non-peptidomimetic covalent binders at submicromolar levels, with IC50 values ranging from 0.118 to 0.582 μM. Interestingly, these compounds were also shown to inhibit PLpro with the same level of IC50 values, but had negligible effect on proteases such as chymotrypsin, cathepsin B, and cathepsin L. Subsequently, the antiviral abilities of these compounds were evaluated in cell-based assays, and compound 6g showed potent antiviral activity with an EC50 value of 7.249 μM. It was proposed that these compounds covalently bind to the catalytic cysteine 145 via a ring-opening metathesis reaction mechanism. To understand this covalent-binding reaction, we chose compound 6a, one of the identified hit compounds, as a representative to investigate the reaction mechanism in detail by combing several computational predictions and experimental validation. The process of ring-opening metathesis was theoretically studied using quantum chemistry calculations according to the transition state theory. Our study revealed that the 2,3,5-substituted [1,2,4]-thiadiazole group could covalently modify the catalytic cysteine in the binding pocket of 3CLpro as a potential warhead. Moreover, 6a was a known GPCR modulator, and our study is also a successful computational method-based drug-repurposing study.

PMID:36702052 | DOI:10.1016/j.ejmech.2023.115129

PLoS Pathog. 2023 Jan 26;19(1):e1011131. doi: 10.1371/journal.ppat.1011131. Online ahead of print.

ABSTRACT

The rapid emergence of SARS-CoV-2 variants of concern, the complexity of infection, and the functional redundancy of host factors, underscore an urgent need for broad-spectrum antivirals against the continuous COVID-19 pandemic, with drug repurposing as a viable therapeutic strategy. Here we report the potential of RNA G-quadruplex (RG4)-targeting therapeutic strategy for SARS-CoV-2 entry. Combining bioinformatics, biochemical and biophysical approaches, we characterize the existence of RG4s in several SARS-CoV-2 host factors. In silico screening followed by experimental validation identify Topotecan (TPT) and Berbamine (BBM), two clinical approved drugs, as RG4-stabilizing agents with repurposing potential for COVID-19. Both TPT and BBM can reduce the protein level of RG4-containing host factors, including ACE2, AXL, FURIN, and TMPRSS2. Intriguingly, TPT and BBM block SARS-CoV-2 pseudovirus entry into target cells in vitro and murine tissues in vivo. These findings emphasize the significance of RG4 in SARS-CoV-2 pathogenesis and provide a potential broad-spectrum antiviral strategy for COVID-19 prevention and treatment.

PMID:36701392 | DOI:10.1371/journal.ppat.1011131

J Neurooncol. 2023 Jan 26. doi: 10.1007/s11060-022-04221-2. Online ahead of print.

ABSTRACT

BACKGROUND: Vestibular schwannoma (VS) is the most common tumour of the cerebellopontine angle and poses a significant morbidity for patients. While many exhibit benign behaviour, others have a more aggressive nature and pattern of growth. Predicting who will fall into which category consistently remains uncertain. There is a need for a better understanding of the molecular landscape, and important subgroups therein, of this disease.

METHODS: We select all vestibular schwannomas from our tumour bank with both methylation and RNA profiling available. Unsupervised clustering methods were used to define two distinct molecular subgroups of VS which were explored using computational techniques including bulk deconvolution analysis, gene pathway enrichment analysis, and drug repurposing analysis. Methylation data from two other cohorts were used to validate our findings, given a paucity of external samples with available multi-omic data.

RESULTS: A total of 75 tumours were analyzed. Consensus clustering and similarity network fusion defined two subgroups ("immunogenic" and "proliferative") with significant differences in immune, stroma, and tumour cell abundance (p < 0.05). Gene network analysis and computational drug repurposing found critical differences in targets of immune checkpoint inhibition PD-1 and CTLA-4, the MEK pathway, and the epithelial to mesenchymal transition program, suggesting a need for subgroup-specific targeted treatment/trial design in the future.

CONCLUSIONS: We leverage computational tools with multi-omic molecular data to define two robust subgroups of vestibular schwannoma with differences in microenvironment and therapeutic vulnerabilities.

PMID:36701029 | DOI:10.1007/s11060-022-04221-2

J Mol Model. 2023 Jan 26;29(2):55. doi: 10.1007/s00894-023-05454-2.

ABSTRACT

CONTEXT: The mutations in the TP53 gene are the most frequent (50-60% of human cancer) genetic alterations in cancer cells, indicating the critical role of wild-type p53 in the regulation of cell proliferation and apoptosis upon oncogenic stress. Most missense mutations are clustered in the DNA-binding core domain, disrupting DNA binding ability. However, some mutations like Y220C occur outside the DNA binding domain and are associated with p53 structure destabilization. Overall, the results of these mutations are single amino acid substitutions in p53 and the production of dysfunctional p53 protein in large amounts, consequently allowing the escape of apoptosis and rapid progression of tumor growth. Thus, therapeutic targeting of mutant p53 in tumors to restore its wild-type tumor suppression activity has immense potential for translational cancer research. Various molecules have been discovered with modern scientific techniques to reactivate mutant p53 by reverting structural changes and/or DNA binding ability. These compounds include small molecules, various peptides, and phytochemicals. TP53 protein is long thought of as a potential target; however, its translation for therapeutic purposes is still in its infancy. The study comprehensively analyzed the therapeutic potential of small phytochemicals from Foeniculum vulgare (Fennel) with drug-likeness and capability to reactivate mutant p53 (Y220C) through molecular docking simulation. The docking study and the stable molecular dynamic simulations revealed juglalin (- 8.6 kcal/mol), retinol (- 9.14 kcal/mol), and 3-nitrofluoranthene (- 8.43 kcal/mol) significantly bind to the mutated site suggesting the possibility of drug designing against the Y220C mutp53. The study supports these compounds for further animal based in vivo and in vitro research to validate their efficacy.

METHODS: For the purposes of drug repurposing, recently in-silico methods have presented with opportunity to rule out many compounds which have less probability to act as a drug based on their structural moiety and interaction with the target macromolecule. The study here utilizes molecular docking via Autodock 4.2.6 and molecular dynamics using Schrodinger 2021 to find potential therapeutic options which are capable to reactive the mutated TP53 protein.

PMID:36700982 | DOI:10.1007/s00894-023-05454-2

Front Cell Infect Microbiol. 2023 Jan 9;12:1044665. doi: 10.3389/fcimb.2022.1044665. eCollection 2022.

ABSTRACT

INTRODUCTION: Leishmaniasis is a neglected tropical disease, with approximately 1 million new cases and 30,000 deaths reported every year worldwide. Given the lack of adequate medication for treating leishmaniasis, drug repositioning is essential to save time and money when searching for new therapeutic approaches. This is particularly important given leishmaniasis's status as a neglected disease. Available treatments are still far from being fully effective for treating the different clinical forms of the disease. They are also administered parenterally, making it challenging to ensure complete treatment, and they are extremely toxic, in some cases, causing death. Triclabendazole (TCBZ) is a benzimidazole used to treat fasciolosis in adults and children. It presents a lower toxicity profile than amphotericin B (AmpB) and is administered orally, making it an attractive candidate for treating other parasitoses. The mechanism of action for TCBZ is not yet well understood, although microtubules or polyamines could potentially act as a pharmacological target. TCBZ has already shown antiproliferative activity against T. cruzi, T. brucei, and L. infantum. However, further investigations are still necessary to elucidate the mechanisms of action of TCBZ.

METHODS: Cytotoxicity assay was performed by MTT assay. Cell inhibition (CI) values were obtained according to the equation CI = (O.D treatment x 100/O.D. negative control). For Infection evaluation, fixated cells were stained with Hoechst and read at Operetta High Content Imaging System (Perkin Elmer). For growth curves, cell culture absorbance was measured daily at 600 nm. For the synergism effect, Fractional Inhibitory Concentrations (FICs) were calculated for the IC50 of the drugs alone or combined. Mitochondrial membrane potential (DYm), cell cycle, and cell death analysis were evaluated by flow cytometry. Reactive oxygen species (ROS) and lipid quantification were also determined by fluorimetry. Treated parasites morphology and ultrastructure were analyzed by electron microscopy.

RESULTS: The selectivity index (SI = CC50/IC50) of TCBZ was comparable with AmpB in promastigotes and amastigotes of Leishmania amazonensis. Evaluation of the cell cycle showed an increase of up to 13% of cells concentrated in S and G2, and morphological analysis with scanning electron microscopy showed a high frequency of dividing cells. The ultrastructural analysis demonstrated large cytoplasmic lipid accumulation, which could suggest alterations in lipid metabolism. Combined administration of TCBZ and AmpB demonstrated a synergistic effect in vitro against intracellular amastigote forms with cSFICs of 0.25.

CONCLUSIONS: Considering that TCBZ has the advantage of being inexpensive and administrated orally, our results suggest that TCBZ, combined with AmpB, is a promising candidate for treating leishmaniasis with reduced toxicity.

PMID:36699729 | PMC:PMC9868945 | DOI:10.3389/fcimb.2022.1044665

Front Neurosci. 2023 Jan 9;16:1084493. doi: 10.3389/fnins.2022.1084493. eCollection 2022.

ABSTRACT

Neurological motor disorders (NMDs) such as Parkinson's disease and Huntington's disease are characterized by the accumulation and aggregation of misfolded proteins that trigger cell death of specific neuronal populations in the central nervous system. Differential neuronal loss initiates the impaired motor control and cognitive function in the affected patients. Although major advances have been carried out to understand the molecular basis of these diseases, to date there are no treatments that can prevent, cure, or significantly delay the progression of the disease. In this context, strategies such as gene editing, cellular therapy, among others, have gained attention as they effectively reduce the load of toxic protein aggregates in different models of neurodegeneration. Nevertheless, these strategies are expensive and difficult to deliver into the patients' nervous system. Thus, small molecules and natural products that reduce protein aggregation levels are highly sought after. Numerous drug discovery efforts have analyzed large libraries of synthetic compounds for the treatment of different NMDs, with a few candidates reaching clinical trials. Moreover, the recognition of new druggable targets for NMDs has allowed the discovery of new small molecules that have demonstrated their efficacy in pre-clinical studies. It is also important to recognize the contribution of natural products to the discovery of new candidates that can prevent or cure NMDs. Additionally, the repurposing of drugs for the treatment of NMDs has gained huge attention as they have already been through clinical trials confirming their safety in humans, which can accelerate the development of new treatment. In this review, we will focus on the new advances in the discovery of small molecules for the treatment of Parkinson's and Huntington's disease. We will begin by discussing the available pharmacological treatments to modulate the progression of neurodegeneration and to alleviate the motor symptoms in these diseases. Then, we will analyze those small molecules that have reached or are currently under clinical trials, including natural products and repurposed drugs.

PMID:36699535 | PMC:PMC9868863 | DOI:10.3389/fnins.2022.1084493

Anticancer Agents Med Chem. 2023 Jan 25. doi: 10.2174/1871520623666230125090815. Online ahead of print.

ABSTRACT

BACKGROUND: Targeting mutated isocitrate dehydrogenase 1 (mIDH1) is one of the key therapeutic strategies for the treatment of glioma. Few inhibitors, such as ivosidenib and vorasidenib, have been identified as selective inhibitors of mIDH1. However, dose-dependent toxicity and limited brain penetration of the blood-brain barrier remain the major limitations of the treatment procedures using these inhibitors.

OBJECTIVE: In the present study, computational drug repurposing strategies were employed to identify potent mIDH1-specific inhibitors from the 11,808 small molecules listed in the DrugBank repository.

METHODS: Tanimoto coefficient (Tc) calculations were initially used to retrieve compounds with structurally similar scaffolds to ivosidenib. The resultant compounds were then subjected to molecular docking to discriminate the binders from the non-binders. The binding affinities and pharmacokinetic properties of the screened compounds were examined using prime Molecular Mechanics-Generalized Born Surface Area (MM-GBSA) and QikProp algorithm, respectively. The conformational stability of these molecules was validated using 100ns molecular dynamics simulation.

RESULTS: Together, these processes led to the identification of three-hit molecules, namely DB12001, DB08026, and DB03346, as potential inhibitors of the mIDH1 protein. Of note, the binding free energy calculations and MD simulation studies emphasized the greater binding affinity and structural stability of the hit compounds towards the mIDH1 protein.

CONCLUSION: The collective evidence from our study indicates the activity of DB12001 against recurrent glioblastoma, which, in turn, highlights the accuracy of our adapted strategy. Hence, we hypothesize that the identified lead molecules could be translated for the development of mIDH1 inhibitors in the near future.

PMID:36698225 | DOI:10.2174/1871520623666230125090815

J Transl Med. 2023 Jan 25;21(1):48. doi: 10.1186/s12967-023-03876-3.

ABSTRACT

BACKGROUND: Drug-target interaction (DTI) prediction has become a crucial prerequisite in drug design and drug discovery. However, the traditional biological experiment is time-consuming and expensive, as there are abundant complex interactions present in the large size of genomic and chemical spaces. For alleviating this phenomenon, plenty of computational methods are conducted to effectively complement biological experiments and narrow the search spaces into a preferred candidate domain. Whereas, most of the previous approaches cannot fully consider association behavior semantic information based on several schemas to represent complex the structure of heterogeneous biological networks. Additionally, the prediction of DTI based on single modalities cannot satisfy the demand for prediction accuracy.

METHODS: We propose a multi-modal representation framework of 'DeepMPF' based on meta-path semantic analysis, which effectively utilizes heterogeneous information to predict DTI. Specifically, we first construct protein-drug-disease heterogeneous networks composed of three entities. Then the feature information is obtained under three views, containing sequence modality, heterogeneous structure modality and similarity modality. We proposed six representative schemas of meta-path to preserve the high-order nonlinear structure and catch hidden structural information of the heterogeneous network. Finally, DeepMPF generates highly representative comprehensive feature descriptors and calculates the probability of interaction through joint learning.

RESULTS: To evaluate the predictive performance of DeepMPF, comparison experiments are conducted on four gold datasets. Our method can obtain competitive performance in all datasets. We also explore the influence of the different feature embedding dimensions, learning strategies and classification methods. Meaningfully, the drug repositioning experiments on COVID-19 and HIV demonstrate DeepMPF can be applied to solve problems in reality and help drug discovery. The further analysis of molecular docking experiments enhances the credibility of the drug candidates predicted by DeepMPF.

CONCLUSIONS: All the results demonstrate the effectively predictive capability of DeepMPF for drug-target interactions. It can be utilized as a useful tool to prescreen the most potential drug candidates for the protein. The web server of the DeepMPF predictor is freely available at http://120.77.11.78/DeepMPF/ , which can help relevant researchers to further study.

PMID:36698208 | DOI:10.1186/s12967-023-03876-3

Front Microbiol. 2023 Jan 5;13:1073419. doi: 10.3389/fmicb.2022.1073419. eCollection 2022.

ABSTRACT

The monkeypox virus (MPXV) has become a major threat due to the increasing global caseload and the ongoing multi-country outbreak in non-endemic territories. Due to limited research in this avenue and the lack of intervention strategies, the present study was aimed to virtually screen bioactive phytochemicals against envelope proteins of MPXV via rigorous computational approaches. Molecular docking, molecular dynamic (MD) simulations, and MM/PBSA analysis were used to investigate the binding affinity of 12 phytochemicals against three envelope proteins of MPXV, viz., D13, A26, and H3. Silibinin, oleanolic acid, and ursolic acid were computationally identified as potential phytochemicals that showed strong binding affinity toward all the tested structural proteins of MPXV through molecular docking. The stability of the docked complexes was also confirmed by MD simulations and MM/PBSA calculations. Results from the iMODS server also complemented the findings from molecular docking and MD simulations. ADME analysis also computationally confirmed the drug-like properties of the phytochemicals, thereby asserting their suitability for consumption. Hence, this study envisions the candidature of bioactive phytochemicals as promising inhibitors against the envelope proteins of the MPXV, serving as template molecules that could further be experimentally evaluated for their efficacy against monkeypox.

PMID:36687601 | PMC:PMC9849581 | DOI:10.3389/fmicb.2022.1073419